Text mining can be used to extract latent semantic content from collections of structured and unstructured text. Data visualization can be used to model the extracted semantic content, which transforms numeric or textual data into graphical data to assist users in understanding underlying semantic principles. For example, clusters group sets of concepts into a graphical element that can be mapped into a graphical screen display. When represented in multi-dimensional space, the spatial orientation of the clusters reflect similarities and relatedness. However, forcibly mapping the display of the clusters into a three-dimensional scene or a two-dimensional screen can cause data misinterpretation. For instance, a viewer could misinterpret dependent relationships between adjacently displayed clusters or erroneously misinterpret dependent and independent variables. As well, a screen of densely-packed clusters can be difficult to understand and navigate, particularly where annotated text labels overlie clusters directly. Other factors can further complicate visualized data perception, such as described in R. E. Horn, “Visual Language: Global Communication for the 21st Century,” Ch. 3, MacroVU Press (1998), the disclosure of which is incorporated by reference.
Physically, data visualization is constrained by the limits of the screen display used. Two-dimensional visualized data can be accurately displayed, yet visualized data of greater dimensionality must be artificially projected into two-dimensions when presented on conventional screen displays. Careful use of color, shape and temporal attributes can simulate multiple dimensions, but comprehension and usability become increasingly difficult as additional layers are artificially grafted into the two-dimensional space and screen density increases. In addition, large sets of data, such as email stores, document archives and databases, can be content rich and can yield large sets of clusters that result in a complex graphical representation. Physical display space, however, is limited and large cluster sets can appear crowded and dense, thereby hindering understandability. To aid navigation through the display, the cluster sets can be combined, abstracted or manipulated to simplify presentation, but semantic content can be lost or skewed.
Moreover, complex graphical data can be difficult to comprehend when displayed without textual references to underlying content. The user is forced to mentally note “landmark” clusters and other visual cues, which can be particularly difficult with large cluster sets. Visualized data can be annotated with text, such as cluster labels, to aid comprehension and usability. However, annotating text directly into a graphical display can be cumbersome, particularly where the clusters are densely packed and cluster labels overlay or occlude the screen display. A more subtle problem occurs when the screen is displaying a two-dimensional projection of three-dimensional data and the text is annotated within the two-dimensional space. Relabeling the text based on the two-dimensional representation can introduce misinterpretations of the three-dimensional data when the display is reoriented. Also, reorienting the display can visually shuffle the displayed clusters and cause a loss of user orientation. Furthermore, navigation can be non-intuitive and cumbersome, as cluster placement is driven by available display space and the labels may overlay or intersect placed clusters.
Therefore, there is a need for providing a user interface for focused display of dense visualized three-dimensional data representing extracted semantic content as a combination of graphical and textual data elements. Preferably, the user interface would facilitate convenient navigation through a heads-up display (HUD) logically provided over visualized data and would enable large-or fine-grained data navigation, searching and data exploration.